Ontologies play a crucial role in organizing and representing knowledge.
However, even current ontologies do not encompass all relevant concepts and
relationships. Here, we explore the potential of large language models (LLM) to
expand an existing ontology in a semi-automated fashion. We demonstrate our
approach on the biomedical ontology SNOMED-CT utilizing semantic relation types
from the widely used UMLS semantic network. We propose a method that uses
conversational interactions with an LLM to analyze clinical practice guidelines
(CPGs) and detect the relationships among the new medical concepts that are not
present in SNOMED-CT. Our initial experimentation with the conversational
prompts yielded promising preliminary results given a manually generated gold
standard, directing our future potential improvements.

使用大型语言模型扩展本体的方法在临床实践指南中探索新的医学概念关系，展示了潜在的初步实验结果和未来改进方向。

大型语言模型是否能补充医学本体中缺失的概念和关系？

Can Large Language Models Augment a Biomedical Ontology with missing  Concepts and Relations?

There is a surge of interest in image scene graph generation (object,
attribute and relationship detection) due to the need of building fine-grained
image understanding models that go beyond object detection. Due to the lack of
a good benchmark, the reported results of different scene graph generation
models are not directly comparable, impeding the research progress. We have
developed a much-needed scene graph generation benchmark based on the
maskrcnn-benchmark and several popular models. This paper presents main
features of our benchmark and a comprehensive ablation study of scene graph
generation models using the Visual Genome and OpenImages Visual relationship
detection datasets. Our codebase is made publicly available at
this https URL

本研究发展了基于 maskrcnn-benchmark 和多个流行模型的场景图像生成基准测试，并通过 Visual Genome 和 OpenImages 视觉关系检测数据集的全面研究来评估场景图像生成模型的主要特性，从而为完善图像理解模型提供了可能。

图像场景图生成（SGG）基准测试

Image Scene Graph Generation (SGG) Benchmark

Most scene graph parsers use a two-stage pipeline to detect visual
relationships: the first stage detects entities, and the second predicts the
predicate for each entity pair using a softmax distribution. We find that such
pipelines, trained with only a cross entropy loss over predicate classes,
suffer from two common errors. The first, Entity Instance Confusion, occurs
when the model confuses multiple instances of the same type of entity (e.g.
multiple cups). The second, Proximal Relationship Ambiguity, arises when
multiple subject-predicate-object triplets appear in close proximity with the
same predicate, and the model struggles to infer the correct subject-object
pairings (e.g. mis-pairing musicians and their instruments). We propose a set
of contrastive loss formulations that specifically target these types of errors
within the scene graph parsing problem, collectively termed the Graphical
Contrastive Losses. These losses explicitly force the model to disambiguate
related and unrelated instances through margin constraints specific to each
type of confusion. We further construct a relationship detector, called RelDN,
using the aforementioned pipeline to demonstrate the efficacy of our proposed
losses. Our model outperforms the winning method of the OpenImages Relationship
Detection Challenge by 4.7\% (16.5\% relative) on the test set. We also show
improved results over the best previous methods on the Visual Genome and Visual
Relationship Detection datasets.

该研究提出了一组对比损失函数用于处理场景图解析中的实体实例混淆和近距离关系歧义问题，并提出了一种称为 RelDN 的关系检测器，该模型在 OpenImages 关系检测挑战赛中相对前一个胜出方法提升了 4.7％（相对 16.5％），在 Visual Genome 和 Visual Relationship Detection 数据集上的结果也得到了改善。